Equitable prediction of respiratory syncytial virus risk in infants
Authors: None The Lancet Digital Health
Journal: The Lancet Digital Health (2023)

Respiratory syncytial virus (RSV) is the most common respiratory virus affecting nearly all infants by the age of 2 years, with the greatest burden on those younger than 6 months. RSV has been estimated globally to cause 34 million acute lower respiratory tract infections in children younger than 5 years annually, with more than 3 million severe cases requiring admission to hospital. Major progress has been made in the development of novel RSV prophylaxis products for infants with the recent USA and UK approvals of both a maternal vaccine and a human monoclonal antibody nirsevimab. These treatments are still likely to be limited to groups at high risk of severe outcomes due to high pricing and difficulties in administration. Infant age and preterm birth are the most important risk factors for severe RSV infection; however, the combined effect of predictors is still poorly understood. A more precise and targeted approach to identifying infants at the highest risk of severe RSV infection is needed. In The Lancet Digital Health, Vartiainen and colleagues describe the largest study to date on RSV hospital admission in infants, using machine learning algorithms to leverage clinical and epidemiological data from 2·72 million children and their family members in two countries, Finland and Sweden, spanning over two decades. They selected 16 variables for their prediction model based on accessibility and generalisability to other countries and demonstrated that the model could identify infants at high risk with good accuracy in the Swedish external validation cohort (c-stat 0·73). Personalised risk scores for infants could enable early intervention, allowing health-care providers to implement preventive measures including targeted administration of treatments, and closer monitoring for at-risk infants. By focusing preventive measures on those at highest risk, health-care systems can reduce hospital admissions, alleviate strain on health-care resources, and optimise the allocation of limited medical interventions. Vartiainen and colleagues' study, like most research on RSV, is done by, and for, populations based in high-income countries (HICs). Although RSV risk prediction is promising, ensuring equitable access to these predictive tools is paramount. Furthermore, the accuracy and generalisability of risk prediction models need to be rigorously tested across diverse populations to avoid biases and disparities in health care. A systematic review in June, 2023, reported only two of 14 studies with data from low-income and middle-income countries (LMICs) or disadvantaged populations in HICs, and there were scarce data available on the effect of respiratory infection severity and recurrence of lung function in these populations. This is especially concerning given that 99% of global RSV-related fatalities occur in infants in LMICs making it the second most common cause of death (after malaria) and this might worsen, given the health risk in infants from ambient air pollution in LMICs. For global health equity to become a reality, data are needed from underserved populations in which the early childhood respiratory infection burden is disproportionately high. This year we mark The Lancet's 200th anniversary by shining a spotlight on the need for governments and health providers to prioritise child health equity within and between countries. In an upcoming webinar, The Lancet and an expert panel will discuss the widening health inequities owing to technology. As such, The Lancet Digital Health calls for the use of open-source technology and where possible, open data, in the development of risk prediction tools that prioritise medical interventions for children most at risk of poor health outcomes. Developers have a responsibility to support the use of their tools for resource-limited populations for the benefit of global health equity. Global agencies such as WHO must prioritise development of technology for RSV risk prediction as a global health resource akin to supporting geopricing and strategies for the deployment of drugs and vaccines for RSV. The emergence of RSV risk prediction and novel RSV prophylaxis offers the potential to transform the way we manage and treat RSV infections in infants. As these predictive models continue to be refined, it is essential to ensure their accessibility and foster collaboration across continents. As new RSV vaccines and preventive interventions become available, we must leverage the momentum to ensure the interventions can reach those at risk. In doing so, we can protect the most vulnerable members of our society and reduce the global burden of RSV-related morbidity and mortality. Risk factors for severe respiratory syncytial virus infection during the first year of life: development and validation of a clinical prediction modelThe identified predictors and the prediction model can be used in guiding RSV immunoprophylaxis in infants, or as a basis for further immunoprophylaxis targeting tools. Full-Text PDF Open Access